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Statistical tools for Regulatory Compliance, Pharmaceutical Control and Development

If you are a practitioner in validation and compliance, you must understand and utilize statistical methods for quality engineering whenever possible. Your work provides the basis for future manufacturing and selection of criteria in pharmaceutical process. Documentation of your work is used in regulatory submissions, regulatory audits, change control, and other activities that support products and processes. Important statistical concepts and methods are essential for making objective decisions related to product quality.

This article provides information on statistical methods useful in daily work situations.

FDA's emphasis on the importance of statistical methods

The FDA Process Validation Guidance released in January 2011 emphasized the importance of statistical expertise is emphasized throughout. Starting from pharmaceutical product process design and development through ongoing commercialization, the guidance has transformed process validation from an individual and singular event to an ongoing continuum. A comprehensive approach to process validation is incorporated in the guidance by including modern concepts, quality-by-design (QbD), process analytical technology (PAT), and risk management.

The statistical analysis and models that are relevant for analytical validation methods are described in the guidance. The other processes such as cleaning, packaging, qualifications such as equipment, facilities, utilities, control systems, hybrid systems such as water, heating, ventilation, and air conditioning [HVAC], and quality systems apply the same principles and approach described above. As measurement itself is a process, the statisticians play a crucial part in the evaluation of the measurement process which is important to the continuation of most other work. Consisting of multiple sub-processes and sub-sub-processes and so on, the pharmaceutical processes have the measurement as the foundation of all of these. The higher-order processes can be studied only if the measurement process is evaluated. When variation and uncertainty exists, statistical methods are the tools that are utilized for better risk-based decision-making.

Process validation defined

"Process validation is defined as the collection and evaluation of data, from the process design stage throughout production, which establishes scientific evidence that a process is capable of consistently delivering quality product. Process validation involves a series of activities taking place over the lifecycle of the product and process. This guidance describes the process validation activities in three stages:

Stage 1 - Process Design: The commercial process is defined during this stage based on knowledge gained through development and scale-up activities.

Stage 2 - Process Qualification: During this stage, the process design is confirmed as being capable of reproducible commercial manufacturing.

Stage 3 - Continued Process Verification: Ongoing assurance is gained during routine production that the process remains in a state of control."

"Effective process validation contributes significantly to assuring drug quality. The basic principle of quality assurance is that a drug should be produced that is fit for its intended use. This principle incorporates the understanding that the following conditions exist:

Quality, safety, and efficacy are designed or built into the product.

Quality cannot be adequately assured merely by in-process and finished-product inspection or testing.

Each step of a manufacturing process is controlled to assure that the finished product meets all quality attributes including specifications."

Regulatory requirements and recommendations

Process validation is an enforceable requirement in the pharmaceutical good manufacturing practices (GMPs) for finished product: Emphasis is laid on the recognition of variation and control in the guidance. Aspects of process validation include both sampling and in-process specifications. Both these areas require statistical analyses. Statistical data gained from the sampling plans must provide assurance that the product batches satisfy the predetermined specifications. The application of statistical procedures must determine the in-process limits. FDA's emphasis on the recognition of variation and associated control is seen from the list of recommendations the guidance provides. A team approach to process validation and the demonstration of expertise in statistics is also recommended.

Although activities in each stage of the process validation are described in the guidance, in practice some activities in different stages might overlap.

Stage 1- Process Design

Stage 1 recommendations address development activities that will ultimately be reflected in the master production record and control records. It includes:

Higher level of sampling

Additional testing

Greater scrutiny of process performance

Above could be extended in certain cases e.g. complexity of process, production volume

Manufacture under normal conditions using routine personnel

The goal of Stage 1 is "To design a process suitable for routine commercial manufacturing that can consistently deliver a product that meets its quality attributes."

Stage 2- Process Qualification

This stage consists of the performance of the commercial process by means of conformance lots

It confirms work of Stage 1 Process Design

Demonstrates that the planned manufacturing process is capable of reproducible commercial manufacture

The acceptability of the developed formulation and process must be demonstrated through increased testing

Discusses design of facility, utilities, and equipment, Process Performance Qualification (PPQ), the PPQ protocol, and PPQ protocol execution and report in Stage 2 all of have a direct connection with specific process validation.

Must be based on sound science and experience as developed in stage 1 studies and activities.

The goal of stage 2 is to show that the process is reproducible and will consistently deliver quality products.

Stage 3 - Continued Process Verification

The guidance clearly demonstrates scope, objectives, and criticality of data analysis and statistical treatment of data in Stage 3. Of special interest is the FDA's recommendations with regard to expertise in statistics.

"An ongoing program to collect and analyze product and process data that relate to product quality must be established. The data collected should include relevant process trends and quality of incoming materials or components, in-process materials, and finished products. The data should be statistically trended and reviewed by trained personnel. The information collected should verify that the quality attributes are being appropriately controlled throughout the process.

We recommend that a statistician with adequate training in statistical process control techniques develop the data collection plan and statistical methods and procedure used in measuring and evaluating process stability and process capability. Procedures should describe how trending and calculations are to be performed and should guard against overreaction to individual events as well as against failure to detect unintended process variability. Production data should be collected to evaluate process stability and capability. The quality unit should review this information. If properly carried out, these efforts can identify variability in the process and/or signal potential process improvements.

"Many tools and techniques, some statistical and others more qualitative, can be used to detect variation, characterize it, and determine the root cause. We recommend that the manufacturer use quantitative statistical methods whenever feasible."

The goal of the third validation stage is continual assurance that the process remains in a state of control (the validated state) during commercial manufacture.

Regulatory expectations with regard to variation, control, and statistics

"A successful validation program depends upon information and knowledge from product and process development. This knowledge and understanding is the basis for establishing an approach to control of the manufacturing process that result in products with the desired quality attributes. Manufacturers should:

Understanding sources of variation

Detect the presence and degrees of variation

Understand the impact of variation in the process and ultimately on product attributes

Control the variation in a manner commensurate with the risk it represents in the process and product.

Each manufacturer should judge whether it has gained sufficient understanding to provide a high degree of assurance in the manufacturing process to justify commercial distribution of the product. Focusing exclusively on qualification efforts with also understanding the manufacturing process and associated variation may not lead to adequate assurance of quality. After establishing and confirming the process, manufacturers must maintain the process in a state of control over the life of the process, even as materials, equipment, production environment, personnel, and manufacturing procedures change.

Manufacturers should use ongoing programs to collect and analyze product and process data to evaluate the state of control of the process. These programs may identify process or product problems or opportunities for process improvements that can be evaluated and implemented through some the activities described in Stages 1 and 2.

Manufacturers of legacy products can take advantage of the knowledge gained from the original process development and qualification work as well as manufacturing experience to continually improve their processes. Implementation of the recommendations in this guidance for legacy product and processes would likely begin with the activities described in Stage 3."

Attend the seminar 'Statistics for Quality Engineering' to learn the different statistical tools necessary to comply with global agencies expectations. Through case study analysis the seminar will examine best practices to provide thoughts and ideas to develop or improve the performance of your current system. Additionally, case studies will explore how your management practices can help or hurt your liability that arises from nonconformance with regulators and Auditors.

The speaker Steven Walfish brings over 20 years of industrial expertise in the development and application of statistical methods for solving complex business issues including data collection, analysis and reporting. He is the President of Statistical Outsourcing Services, a consulting company that provides statistical analysis and training to FDA regulated industries. Prior to starting Statistical Outsourcing Services, Mr. Walfish was the Senior Manager Biostatistics, Non-clinical at Human Genome Sciences in Rockville MD. Prior to joining HGS, Mr. Walfish was a Senior Associate at PricewaterhouseCoopers specializing in the pharmaceutical industry. Mr. Walfish was Manager of Non-Clinical Statistics at Chiron Diagnostics.